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Feature Papers in Wearables 2023
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Feature Papers in Wearables 2023

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5628

Special Issue Editor

Dr. Roozbeh Ghaffari

grade E-Mail Website
Guest Editor
Querrey Simpson Institute for Bioelectronics, Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA
Interests: flexible electronics; biosensors; wearable computing; MEMS; neuroscience; microfluidics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Wearables section is now compiling a collection of papers submitted exclusively by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. The Special Issue engages in topics such as emerging wearable systems with integrated sensors (motion, ECG, HRV, GSR, blood pressure, biochemical sensors, and others); actuators (drug delivery, electrical stimulus, thermal actuator and phototherapy); and data analytics engines for addressing key chronic medical conditions, diseases, health diagnostics, stress (mental and physical), wellness, and fitness applications.

The purpose of this Special Issue is to publish a set of papers that typifies the very best insightful and influential original articles or reviews in which our section’s EBMs and outstanding scholars discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be published in a printed edition book after the deadline and will be extensively promoted.

Taking this opportunity, we would also like to call on more excellent scholars to join the Wearables section to contribute to the development of this field.

Dr. Roozbeh Ghaffari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

14 pages, 4260 KiB  
Article
Piezoresistive Porous Composites with Triply Periodic Minimal Surface Structures Prepared by Self-Resistance Electric Heating and 3D Printing
by Ke Peng, Tianyu Yu, Pan Wu and Mingjun Chen
Sensors 2024, 24(7), 2184; https://doi.org/10.3390/s24072184 - 28 Mar 2024
Viewed by 383
Abstract
Three-dimensional flexible piezoresistive porous sensors are of interest in health diagnosis and wearable devices. In this study, conductive porous sensors with complex triply periodic minimal surface (TPMS) structures were fabricated using the 3D printed sacrificial mold and enhancement of MWCNTs. A new curing [...] Read more.
Three-dimensional flexible piezoresistive porous sensors are of interest in health diagnosis and wearable devices. In this study, conductive porous sensors with complex triply periodic minimal surface (TPMS) structures were fabricated using the 3D printed sacrificial mold and enhancement of MWCNTs. A new curing routine by the self-resistance electric heating was implemented. The porous sensors were designed with different pore sizes and unit cell types of the TPMS (Diamond (D), Gyroid (G), and I-WP (I)). The impact of pore characteristics and the hybrid fabrication technique on the compressive properties and piezoresistive response of the developed porous sensors was studied. The results indicate that the porous sensors cured by the self-resistance electric heating could render a uniform temperature distribution in the composites and reduce the voids in the walls, exhibiting a higher elastic modulus and a better piezoresistive response. Among these specimens, the specimen with the D-based structure cured by self-resistance electric heating showed the highest responsive strain (61%), with a corresponding resistance response value of 0.97, which increased by 10.26% compared to the specimen heated by the external heat sources. This study provides a new perspective on design and fabrication of porous materials with piezoresistive functionalities, particularly in the realm of flexible and portable piezoresistive sensors. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
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11 pages, 1220 KiB  
Article
Embodimetrics: A Principal Component Analysis Study of the Combined Assessment of Cardiac, Cognitive and Mobility Parameters
by Andrea Chellini, Katia Salmaso, Michele Di Domenico, Nicola Gerbi, Luigi Grillo, Marco Donati and Marco Iosa
Sensors 2024, 24(6), 1898; https://doi.org/10.3390/s24061898 - 15 Mar 2024
Viewed by 863
Abstract
There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between cognitive, [...] Read more.
There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac, and motor variables using principal component analysis (PCA). Additionally, we assessed changes in the relationships among these variables after a two-month intervention aimed at improving the perception–action link. At baseline, PCA correctly identified three components: one related to cardiac variables, one to trunk motion, and one to Stroop task performance. After the intervention, only two components were found, with trunk symmetry and range of motion, accuracy, time to complete the Stroop task, and low-frequency heart rate variability aggregated into a single component using PCA. Artificial neural network analysis confirmed the effects of both HRV and motor behavior on cognitive Stroop task performance. This analysis suggested that this protocol was effective in investigating embodied cognition, and we defined this approach as “embodimetrics”. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
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17 pages, 3429 KiB  
Article
Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain
by Trung C. Phan, Adrian Pranata, Joshua Farragher, Adam Bryant, Hung T. Nguyen and Rifai Chai
Sensors 2024, 24(4), 1337; https://doi.org/10.3390/s24041337 - 19 Feb 2024
Viewed by 801
Abstract
Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning [...] Read more.
Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
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19 pages, 9204 KiB  
Article
A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor
by Tsige Tadesse Alemayoh, Jae Hoon Lee and Shingo Okamoto
Sensors 2023, 23(24), 9841; https://doi.org/10.3390/s23249841 - 15 Dec 2023
Viewed by 774
Abstract
In this study, we introduce a novel framework that combines human motion parameterization from a single inertial sensor, motion synthesis from these parameters, and biped robot motion control using the synthesized motion. This framework applies advanced deep learning methods to data obtained from [...] Read more.
In this study, we introduce a novel framework that combines human motion parameterization from a single inertial sensor, motion synthesis from these parameters, and biped robot motion control using the synthesized motion. This framework applies advanced deep learning methods to data obtained from an IMU attached to a human subject’s pelvis. This minimalistic sensor setup simplifies the data collection process, overcoming price and complexity challenges related to multi-sensor systems. We employed a Bi-LSTM encoder to estimate key human motion parameters: walking velocity and gait phase from the IMU sensor. This step is followed by a feedforward motion generator-decoder network that accurately produces lower limb joint angles and displacement corresponding to these parameters. Additionally, our method also introduces a Fourier series-based approach to generate these key motion parameters solely from user commands, specifically walking speed and gait period. Hence, the decoder can receive inputs either from the encoder or directly from the Fourier series parameter generator. The output of the decoder network is then utilized as a reference motion for the walking control of a biped robot, employing a constraint-consistent inverse dynamics control algorithm. This framework facilitates biped robot motion planning based on data from either a single inertial sensor or two user commands. The proposed method was validated through robot simulations in the MuJoco physics engine environment. The motion controller achieved an error of ≤5° in tracking the joint angles demonstrating the effectiveness of the proposed framework. This was accomplished using minimal sensor data or few user commands, marking a promising foundation for robotic control and human–robot interaction. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
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11 pages, 4628 KiB  
Article
Validation of Inertial Measurement Units for Analyzing Golf Swing Rotational Biomechanics
by Sung Eun Kim, Jayme Carolynn Burket Koltsov, Alexander Wilder Richards, Joanne Zhou, Kornel Schadl, Amy L. Ladd and Jessica Rose
Sensors 2023, 23(20), 8433; https://doi.org/10.3390/s23208433 - 13 Oct 2023
Viewed by 1126
Abstract
Training devices to enhance golf swing technique are increasingly in demand. Golf swing biomechanics are typically assessed in a laboratory setting and not readily accessible. Inertial measurement units (IMUs) offer improved access as they are wearable, cost-effective, and user-friendly. This study investigates the [...] Read more.
Training devices to enhance golf swing technique are increasingly in demand. Golf swing biomechanics are typically assessed in a laboratory setting and not readily accessible. Inertial measurement units (IMUs) offer improved access as they are wearable, cost-effective, and user-friendly. This study investigates the accuracy of IMU-based golf swing kinematics of upper torso and pelvic rotation compared to lab-based 3D motion capture. Thirty-six male and female professional and amateur golfers participated in the study, nine in each sub-group. Golf swing rotational kinematics, including upper torso and pelvic rotation, pelvic rotational velocity, S-factor (shoulder obliquity), O-factor (pelvic obliquity), and X-factor were compared. Strong positive correlations between IMU and 3D motion capture were found for all parameters; Intraclass Correlations ranged from 0.91 (95% confidence interval [CI]: 0.89, 0.93) for O-factor to 1.00 (95% CI: 1.00, 1.00) for upper torso rotation; Pearson coefficients ranged from 0.92 (95% CI: 0.92, 0.93) for O-factor to 1.00 (95% CI: 1.00, 1.00) for upper torso rotation (p < 0.001 for all). Bland–Altman analysis demonstrated good agreement between the two methods; absolute mean differences ranged from 0.61 to 1.67 degrees. Results suggest that IMUs provide a practical and viable alternative for golf swing analysis, offering golfers accessible and wearable biomechanical feedback to enhance performance. Furthermore, integrating IMUs into golf coaching can advance swing analysis and personalized training protocols. In conclusion, IMUs show significant promise as cost-effective and practical devices for golf swing analysis, benefiting golfers across all skill levels and providing benchmarks for training. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
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16 pages, 4282 KiB  
Article
Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice
by Mingqi Li, Gabrielle Scronce, Christian Finetto, Kristen Coupland, Matthew Zhong, Melanie E. Lambert, Adam Baker, Feng Luo and Na Jin Seo
Sensors 2023, 23(13), 6110; https://doi.org/10.3390/s23136110 - 03 Jul 2023
Cited by 1 | Viewed by 1031
Abstract
Upper extremity hemiplegia is a serious problem affecting the lives of many people post-stroke. Motor recovery requires high repetitions and quality of task-specific practice. Sufficient practice cannot be completed during therapy sessions, requiring patients to perform additional task practices at home on their [...] Read more.
Upper extremity hemiplegia is a serious problem affecting the lives of many people post-stroke. Motor recovery requires high repetitions and quality of task-specific practice. Sufficient practice cannot be completed during therapy sessions, requiring patients to perform additional task practices at home on their own. Adherence to and quality of these home task practices are often limited, which is likely a factor reducing rehabilitation effectiveness post-stroke. However, home adherence is typically measured by self-reports that are known to be inconsistent with objective measurement. The objective of this study was to develop algorithms to enable the objective identification of task type and quality. Twenty neurotypical participants wore an IMU sensor on the wrist and performed four representative tasks in prescribed fashions that mimicked correct, compensatory, and incomplete movement qualities typically seen in stroke survivors. LSTM classifiers were trained to identify the task being performed and its movement quality. Our models achieved an accuracy of 90.8% for task identification and 84.9%, 81.1%, 58.4%, and 73.2% for movement quality classification for the four tasks for unseen participants. The results warrant further investigation to determine the classification performance for stroke survivors and if quantity and quality feedback from objective monitoring facilitates effective task practice at home, thereby improving motor recovery. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
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